
Explore how data quality reflects the real world, covering accuracy, completeness, consistency, timeliness, relevance, and uniqueness, and learn how poor quality harms decisions, resources, and revenue.
Explore the six dimensions of data quality—accuracy, completeness, consistency, timeliness, relevance, and uniqueness—and apply data validation, audits, profiling, and governance to improve data reliability and actionable insights.
Map data sources and destinations, define field meanings and constraints, and set retention, sensitivity, and quality classifications. Trace end-to-end data lineage to anticipate issues from entry, integration, and storage.
Profile your data to understand quality by identifying missing, duplicate, inconsistent values, and spotting outliers. Use visualizations like scatter plots and heatmaps, plus summary statistics to reveal patterns and trends.
Analyze data with traditional statistics and descriptive analytics to understand distributions, outliers, and patterns. Apply data quality rules, cross-source validation, sampling, and anomaly detection to identify discrepancies and improve accuracy.
Explore automated data pipelines that extract, transform, and load data with built-in quality checks and cleaning. Classify data as gold, silver, or bronze to ensure reliable analytics.
Establish standardized data-entry, metadata, naming, and release processes to support data quality, then continuously audit data, metadata, lineage, and the data life cycle for accuracy and completeness.
Build a culture of data ownership and accountability by defining roles, empowering employees, investing in quality tools, and training everyone to interpret data and drive data-driven decisions.
Define data goals and a to-be state, profile the as-is data to reveal gaps, and plan a quality initiative. Align data owners and stewards to implement metadata, standards, and training.
Monitor data quality continually as it evolves with usage and systems. Assess data sets across six dimensions and audit processes to resolve issues and sustain trust for data-driven decisions.
This course is designed to equip participants with the knowledge and tools necessary to understand, evaluate, and enhance the quality of data within organisational contexts. In today's data-driven world, the significance of reliable, accurate, and high-quality data cannot be overstated.
The course is not designed to make you an expert but rather to give you a good grounding in data quality, it's importance and to give you some actionable steps to take after the course.
Throughout this course, participants will delve into the fundamental concepts of data quality, exploring its definition and significance in various operational and analytical contexts.
Participants will learn practical methodologies and tools for assessing data quality within their organisations and then delve into strategies for enhancing data quality, covering both process-oriented changes and organisational shifts necessary to establish a culture of data quality improvement.
We'll look at utilising data pipelines to improve and monitor data quality.
No previous experience is required. By the end of this course you should have a good understanding of some of the steps we can take to improve and monitor data quality.
Join me on this exciting journey towards mastering data quality management and unleash the potential of your data assets.